1 Steven Goodman GOES-R Program Senior Scientist NOAA/NESDIS (with contributions from our many partners) NOAA igh Impact Weather Workshop Norman, OK 24 February, 2011 Geostationary Lightning Mapper (GLM)

GOES-R Spacecraft Specifications Size ~5.5 meters (from launch vehicle interface to top of ABI) Mass Satellite (spacecraft and payloads) dry mass <2800kg Power Capacity >4000W at end-of-life (includes accounting for limited array degradation) Lockheed-Martin Space Systems Co (LMSSC) of Newtown, PA is primary contractor Advanced Baseline Imager (ABI) Geostationary Lightning Mapper (GLM) Solar UV Imager (SUVI) Magnetometer Space Environment In-situ Suite (SEISS) Extreme UV/X-ray Irradiance Sensor (EXIS) Unique Payload Services: High Rate Information Transmission/Emergency Managers Weather Information Network (HRIT/EMWIN) Data Collection System (DCS) Search and Rescue Satellite-aided Tracking (SARSAT) Repeater GOES-R Rebroadcast (GRB) Current Status Design activities progressing well Spacecraft System Definition Review (SDR) completed March 9-10, 2010 Spacecraft baseline established in April 2010 Preliminary Design Review (PDR) held January 18-20,

Geostationary Lightning Mapper (GLM) 3 Lightning with Hurricane Katrina and Tornadic Storms over Oklahoma Optical Assembly Sensor Unit Mechanical Support Structure Metering tube Detects total lightning: in cloud, cloud to cloud, and cloud to ground – Aids in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. – Currently no ocean coverage, and limited land coverage in dead zones

GOES-R GLM Coverage LIS/OTD Combined Lightning GLM Characteristics Staring CCD imager (1372x1300 pixels) Near uniform spatial resolution/ coverage up to 52 deg lat - 8 km nadir -14 km edge fov 70-90% flash detection Single band nm 2 ms frame rate 7.7 Mbps downlink data rate < 20 sec product latency 1-minute of observations from TRMM/LIS

Volcanic Lightning: Eruption of Redoubt Volcano NM Tech

Volcanic Lightning: Eyjafjallajökull Eruption, Iceland 2010 Reykjavik NM Tech

Physical Basis: Lightning Connection to Thunderstorm Updraft, Storm Growth and Decay Total Lightning —responds to updraft velocity and concentration, phase, type of hydrometeors, integrated flux of particles WX Radar — responds to concentration, size, phase, and type of hydrometeors- integrated over small volumes Microwave Radiometer — responds to concentration, size, phase, and type of hydrometeors — integrated over depth of storm (85 GHz ice scattering) VIS / IR — cloud top height/temperature, texture, optical depth Air Mass Storm 20 July Figure from Gatlin and Goodman, JTECH, Jan adapted from Goodman et al, 1988; Kingsmill and Wakimoto, 1991

Laboratory charging results for temperature as a function of cloud water content (Takahashi et al., 1978) Laboratory Cloud Charging Results Large ice particles charge negatively

0 o C Physical Basis: Flash Rate Coupled to Mass in the Mixed Phase Region (Cecil et al., Mon. Wea. Rev. 2005) Process physics understood Storm-scale model with explicit microphysics and electrification (Mansell) Ice flux drives lightning Physical basis for improved forecasts IC flash rate controlled by graupel (ice mass) production (and vertical velocity) TRMM PR and LIS

EFPEWP GOES-R PG Experimental Forecast Program Experimental Warning Program Prediction of hazardous weather events from a few hours to a week in advance Detection and prediction of hazardous weather events up to several hours in advance NOAA’s Hazardous Weather Testbed

GOES-R Proving Ground – What is the GOES-R Proving Ground? Collaborative effort between the GOES-R Program Office, selected NOAA Cooperative Institutes, NASA SPoRT, NWS forecast offices, NCEP National Centers, JCSDA, and NOAA Testbeds. Where proxy and simulated GOES-R products are tested, evaluated and integrated into operations before the GOES-R launch A key element of GOES-R User Readiness (Risk Mitigation)

Total Lightning Detection Pseudo-GLM – Data from ground-based total lightning detection networks Huntsville, AL; Washington, DC; Melbourne, FL; and Norman, OK – Raw data sorted into flashes and interpolated to an 8km grid – Running 2-minute average Simulated lightning threat – Based on NSSL-WRF 0Z 4km data – Estimates total lightning from vertical ice content and flux within cloud objects (see McCaul et al., 2009)

13 Forecaster AWIPS display of PGLM flash extent density product and IR image over Central Tennessee and Northern Alabama at 2215 UTC on 9 June The overlay of PGLM on IR allowed the forecaster to focus on the most active convective cores. Pseudo-Geostationary Lightning Mapper (PGLM) The real-time lightning data was available in 1 or 2-minute intervals and sorted into flashes using algorithms available through Warning Decision Support System – Integrated Information (WDSS-II). Following flash sorting, a Flash Extent Density product was created at 8-km resolution to match that expected by the GOES-R GLM. NWS forecasters evaluated the PGLM product during both real-time operations and for an archive event. The PGLM product was available as a running 2-minute average at 1-minute updates within AWIPS. The PGLM products provided a strong support tool for the forecasters and helped increase forecaster confidence to warn or not warn on a storm. The lightning data was often noted as perhaps being more important with pulse storms or near-severe situations where lightning would be more clearly indicative of important updraft fluctuations. Forecasters viewed future GLM data as a “great tool” or a possible “mainstream product” for “situational awareness” in “making sure no dangerous cells are being missed.”

14 WRF Lightning Threat Comparison with NEXRAD and LMA Observations Direct comparison of the NSSL WRF 27 h forecast at 03Z on 25 April 2010 with the 0300 UTC radar and Lightning Mapping Array (LMA, top row) and model composite reflectivity and max-hourly LFA flash extensity density (bottom row). The LFA in the lower-right shows the tracks of storms in the previous forecast hour. The radar data are a merged composite of HTX,OHX,GWX,BMX and FFC, with the plotted values being the largest of the 5 radars at each pixel.

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Total Lightning Increases with Storm Growth and Updraft Intensification 16

Lightning Jump Algorithm Status AlgorithmPODFARCSIHSS Gatlin and Goodman 90%66%33%0.49 Gatlin 4597%64%35%0.52 2σ2σ87%33%61%0.75 3σ3σ56%29%45%0.65 Threshold 1072%40%49%0.66 Threshold 883%42%50%0.67 Six separate lightning jump configurations tested Case study expansion: – 107 T-storms analyzed 38 severe 69 non-severe The “2σ” configuration yielded best results – POD beats NWS performance statistics (80-90%); – FAR even better i.e.,15% lower (Barnes et al. 2007) Caveat: Large difference in sample sizes, more cases are needed to finalize result. M.S. Thesis completed and study accepted to JAMC (Schultz, Petersen, Carey 2009); forms the conceptual basis of the lightning jump ATBD Thunderstorm breakdown: North Alabama – 83 storms Washington D.C. – 2 storms Houston TX – 13 storms Dallas – 9 storms

LJA Case Expansion Since, we’ve expanded to 638 thunderstorms – Primarily from N. Alabama (537) – Also included Washington D.C. (49 and counting) Oklahoma (30 and counting) STEPS (22) Regional expansion has proven robust – POD: 82%, FAR 35%, avg. lead time: 22 mins. Courtesy Chris Schultz, UAH

HWT Blog 19 EWP ready to go... 5/19/2010 EWP ready to go... 5/19/2010 Some notes from the briefing... The NSSL-WRF lightning threat forecast was shown to the forecasters for this evening and it helped us identify which storms may have stronger updrafts because of their increased lightning output, which we couldn't necessarily determine from the synthetic satellite or radar output. Thursday, May 20, 2010 At 1:30 PM, the North Alabama Lightning Mapping Array (NALMA) showed lightning activity along the northern Mississippi-Alabama border. The 00Z 20 May NSSL-WRF run in support of the NSSL/SPC EFP shows continued evolution of this convection toward central Alabama by 00-02Z this evening. The lightning threat field in the NSSL-WRF using the McCaul blended vertically integrated ice / graupel flux method shows lightning activity extending north-south through Alabama at 1Z. The predicted flash rates are somewhat less over the far northern part of the domain.

HWT: Forecaster Feedback from 2010 “We saw several instances where the total lightning was picking up on storms before the AWIPS lightning [NLDN] program picked up on them. One could see the utility of this in the future, bringing with it a potential for lighting statements and potentially lightning based warnings.” -Pat Spoden (SOO, NWSFO Paducah, KY)   K. Kuhlman

HWT: Forecaster Feedback from 2010 “[GLM] will also prove very beneficial as we get more into decision support services, especially to support the safety of responders to incidents who are exposed to lightning hazards.” -- Frank Alsheimer (SOO, NWSFO Charleston, SC)  “lightning data provide a reassurance that I can see leading me to make a warning decision a little earlier due to having more confidence in imminent severe weather.”  “would be of great benefit to aviation forecasting in those situations where there is a developing shower or embedded thunderstorms in stratiform rain.” K. Kuhlman

Suggestions for Future Testing: Additional Products: – Rate of change of flash rate (as a gridded product, not a line graph) More Events: – Winter Weather (convective snow bands) – Landfalling tropical cyclones – Fire & Aviation Applications Increased guidance from research – Flash rates expected with different convective modes and associated severe weather occurrence – Relationship of flash density with radar signatures typical of severe weather K. Kuhlman

Aviation-Convective Weather Hazards Since there are few surface-based radar and/or other meteorological observations covering most of the oceans, convective intensity and associated aviation hazard potential (i.e., turbulence, icing, lightning, volcanic ash) are evaluated using satellites Ultimately, the goal is a combined GOES-R GLM/ABI algorithm for the detection of aviation hazards associated with convection. Such an algorithm should improve aviation routing and safety in the vicinity of thunderstorms, thus reducing the number of related incident reports and suspected storm-related accidents

GLM Proxy over Remote Regions Within the lightning science and applications community, there is a need for lightning data over the oceans and other remote regions (i.e., global). – E.g., enroute aviation applications, hurricane studies, GLM proxy LIS (OTD) is an ideal proxy for GLM but there is no temporal continuity (snapshots). What are the potential options for global (or very large remote region)? – World Wide Lightning Location Network (WWLLN) – Vaisala Global Lightning Dataset (GLD360) – Earth Networks (AWS) WeatherBug (WTLN) – Other ground-based VLF-based long-range networks (WSI TOA, UK ATDnet) WWLLN is more mature and better characterized – 30% cloud-to-ground (CG) flash detection efficiency (DE) for peak current > 30 kA – CG Flash Location Accuracy (LA) ~ 15 – 30 km

* Overlap days: 8/17/2010, 8/24/2010 – 9/15/2010, 9/21/2010 – 10/5/2010, 10/12/2010 – 10/31/2010 NLDN Lightning: August-September-October 2010

* Overlap days: 8/17/2010, 8/24/2010 – 9/15/2010, 9/21/2010 – 10/5/2010, 10/12/2010 – 10/31/2010 WTLN Lightning: August-September-October 2010

* Overlap days: 8/17/2010, 8/24/2010 – 9/15/2010, 9/21/2010 – 10/5/2010, 10/12/2010 – 10/31/2010 GLD360 Lightning: August-September-October 2010

LIS/OTD Total Lightning Climatology: August-September-October Of critical importance- need to understand commercial ground-based lightning detection systems for 1) GLM proxy data set development, and 3) potential use in GLM on-orbit cal/val, and 3) potential applications using integrated observing systems

29 Courtesy, Nikki Hembury

Previous lightning data assimilation work: – Alexander et al., 1999; Chang et al (latent heating) – Papadopoulos et al., 2005 (moisture profiles) – Mansell et al., 2006, 2007 (BL moisture and updraft speed; NLDN/LMA convective trigger switch for Kain-Fritsch) – Weygandt et al., 2006, 2008 (cloud and moisture fields-lightning- reflectivity relationship to create a latent heating-based temperature tendency field, applied to RUC /HRRR during a pre-forecast diabatic digital filter initialization) – Pessi and Businger, 2009 (Vaisala Pacnet long-range lightning data over the open ocean- tropical cyclones, oceanic storms) Workshop on Lightning Modeling and Data Assimilation (2010) – Lightning Data Assimilation into NWP Models

31 March 13, 1993 Superstorm (Alexander et al., 1999 MWR) Lightning Data Assimilation: Reduces Forecast Error Lightning assimilated via latent heat transfer functional relationship

Rain rate transfer function Establish a Lightning – Rain Rate Transfer Function TMI GPROF Rain Rate Sferics Rain Rate LTG-RR converted into parabolic Latent Heat profile centered at 500 mb

The log-normal relationship between lightning rate and rainfall intensity derived from TRMM and PacNet data is the key to use of lightning data in numerical weather prediction models (Pessi and Businger). Lightning vs. Convective Rainfall 1. Pessi, A. T. et al., 2008: J. Atmos. and Ocean. Tech., 26, 145– Pessi, A. T., and S. Businger, 2009: J. Appl. Meteor., 48, 833– Squires, K. and S. Businger, 2008: Mon. Wea. Rev., 136, 1706– Pessi, A. T., and S. Businger, 2009: Mon. Wea. Rev., 137,

Control Lightning Assimilation Surface analysis Valid 1200 UTC 19 December 2002 L 12-Hour Forecast of Sea-Level Pressure and 3-h Rainfall L Steve Businger

Advection of High Theta-e Air into the Storm Center Upper figure: (a) CTRL, (b) LDA Wind speed at 400 hPa (m/s, shaded) Temperature at 400 hPa (K, contours) Latent heating, as informed by the high lightning rates, increased temperature and ∇ T across the front. This resulted in increased along-front winds, consistent with thermal wind balance. Lower figure: Difference between LDA and CTRL in: Virtual temperature (K, shaded) Geopotential height (m, contours) Enhanced advection of warm air over the storm center dropped the surface pressure hydrostatically. Steve Businger

Field Campaign – Leverage observing assets associated with CHUVA with U.S. supplied portable LMA network (and European supplied LINET) to generate proxy data sets for GLM and ABI that include total lightning (LIS and ground-based) and SEVIRI. – Allow GLM and Combined AWG/Science teams to better address and assess several areas of on-going research 36 Science Objectives: o Algorithm and Proxy Data Validation o Validation Systems Performance Assessment o Storm Electrification/Physics o Applications for GLM+ABI+… Key scientific measurements: VHF 3-D Lightning Mapping Array (LMA), LINET, TRMM/LIS, MSG SEVERI (ABI proxy data), high speed digital video, VLF lightning networks, dual-pol radar, electric field change, airplane in-situ microphysics, and ancillary meteorological data CHUVA Ground Validation IOP Sao Paulo, Brazil TRMM/LISMSG/SEVERI

Partners and Collaborators GLM Science Team Richard Blakeslee, NASA (LMA lead) Larry Carey, Jeff Bailey, UAH (NOAA funded to deploy LMA) John Hall, UAH (Web support for real time network operations) Monte Bateman, USRA (proxy data, other analyses) Many others (and other GOES-R teams) (algorithm and proxy val) InPE (CPTEC, ELAT) and USP Luiz Machado, InPE/CPTEC (overall CHUVA lead) Rachel Albrecht, InPE/CPTEC Carlos Morales, USP (Electrification processes lead) Osmar Pinto Jr., InPE/ELAT Research Group EUMETSAT MTG Lighting Imager Science Team Hartmut Hoeller, DLR (LINET lead) (Collaborator) Commercial Data Providers Vaisala LS8000, WeatherBug WTLN (Collaborator) 37

Summary The GOES-R GLM offers information on high impact weather phenomena-storm growth, decay and intensity The GOES-R Proving Ground provides mechanism to: – Involve CIs, AWG, National Centers, NOAA Testbeds and WFOs in user readiness – Get prototype GOES-R products in hands of forecasters – Keep lines of communication open between developers and forecasters – Allow end user to have say in final product, how it is displayed and integrated into operations Opportunities for FDP/RDP demonstrations of experimental satellite and lightning products